A Probabilistic Framework for Representing Dialog Systems and Entropy-Based Dialog Management Through Dynamic Stochastic State Evolution

In this paper, we present a probabilistic framework for goal-driven spoken dialog systems. A new dynamic stochastic state (DS-state) is then defined to characterize the goal set of a dialog state at different stages of the dialog process. Furthermore, an entropy minimization dialog management (EMDM) strategy is also proposed to combine with the DS-states to facilitate a robust and efficient solution in reaching a user's goals. A song-on-demand task, with a total of 38 117 songs and 12 attributes corresponding to each song, is used to test the performance of the proposed approach. In an ideal simulation, assuming no errors, the EMDM strategy is the most efficient goal-seeking method among all tested approaches, returning the correct song within 3.3 dialog turns on average. Furthermore, in a practical scenario, with top five candidates to handle the unavoidable automatic speech recognition (ASR) and natural language understanding (NLU) errors, the results show that only 61.7% of the dialog goals can be successfully obtained in 6.23 dialog turns on average when random questions are asked by the system, whereas if the proposed DS-states are updated with the top five candidates from the SLU output using the proposed EMDM strategy executed at every DS-state, then a 86.7% dialog success rate can be accomplished effectively within 5.17 dialog turns on average. We also demonstrate that entropy-based DM strategies are more efficient than non-entropy based DM. Moreover, using the goal set distributions in EMDM, the results are better than those without them, such as in sate-of-the-art database summary DM.